Phase R.3：动态权重增强

日期：2026-04-03 状态：已规划依赖：R.1（Token 感知分块）工作量：4.5 天

1. 本阶段目的

根据查询特性动态调整检索策略，支持核心标签加权。

2. 核心任务

Task R.3.1：实现查询特性分析

目标： 分析查询类型（代码/表格/对话式）

新增文件： backend/app/services/query_analyzer.py

import re
from dataclasses import dataclass

@dataclass
class QueryProfile:
    logic_depth: float      # 逻辑深度 (0-1): 意图明确程度
    is_code_related: bool  # 是否代码相关
    is_table_related: bool # 是否表格相关
    keyword_density: float  # 关键词密度
    is_conversational: bool  # 是否对话式查询

class QueryAnalyzer:
    CODE_KEYWORDS = {'code', 'function', 'class', 'api', 'python', 'js', 'bug', '函数', '代码'}
    TABLE_KEYWORDS = {'table', 'sheet', 'excel', 'csv', 'column', 'row', '数据', '统计', '表格', '列', '行'}
    
    def analyze(self, query: str) -> QueryProfile:
        words = set(re.findall(r'\w+', query.lower()))
        
        return QueryProfile(
            logic_depth=self._calc_logic_depth(query),
            is_code_related=bool(words & self.CODE_KEYWORDS),
            is_table_related=bool(words & self.TABLE_KEYWORDS),
            keyword_density=len(words) / max(len(query), 1),
            is_conversational=self._is_conversational(query),
        )
    
    def _calc_logic_depth(self, query: str) -> float:
        """计算逻辑深度：问句、具体名词越多越聚焦"""
        question_markers = ['how', 'why', 'what', 'which', '哪个', '如何', '为什么', '怎么']
        has_question = any(q in query.lower() for q in question_markers)
        has_specific_terms = len(re.findall(r'\w{5,}', query)) > 3
        return 0.8 if (has_question and has_specific_terms) else 0.5
    
    def _is_conversational(self, query: str) -> bool:
        """判断是否为对话式查询"""
        conversational_patterns = ['你', '我想', '能不能', '可以帮我', 'what do you think']
        return any(p in query for p in conversational_patterns)

Task R.3.2：实现动态 Reranker

目标： 根据查询类型动态调整语义/关键词/标题权重

新增文件： backend/app/services/dynamic_reranker.py

import json
from dataclasses import dataclass

class DynamicReranker:
    """动态 Reranker，根据查询特性调整权重"""
    
    def rerank(
        self, 
        query: str, 
        results: list[SearchResult],
        analyzer: QueryAnalyzer
    ) -> list[SearchResult]:
        profile = analyzer.analyze(query)
        weights = self._get_weights(profile)
        beta = self._calc_beta(profile)
        
        scored = []
        for r in results:
            score = r.score * weights["semantic"]
            score += self._keyword_score(query, r.content) * weights["keyword"]
            score += self._title_score(query, r.document_title) * weights["title"]
            
            # 表格内容加分
            if profile.is_table_related:
                meta = json.loads(r.metadata_ or "{}")
                if meta.get("content_type") == "table_schema":
                    score += 0.25
                elif meta.get("content_type") == "table_rows":
                    score += 0.15
            
            score *= beta
            scored.append((score, r))
        
        scored.sort(key=lambda x: x[0], reverse=True)
        return [r for _, r in scored]
    
    def _get_weights(self, profile: QueryProfile) -> dict:
        if profile.is_code_related:
            return {"semantic": 0.55, "keyword": 0.35, "title": 0.10}
        elif profile.is_table_related:
            return {"semantic": 0.50, "keyword": 0.30, "title": 0.20}
        elif profile.is_conversational:
            return {"semantic": 0.85, "keyword": 0.10, "title": 0.05}
        else:
            return {"semantic": 0.70, "keyword": 0.20, "title": 0.10}
    
    def _calc_beta(self, profile: QueryProfile) -> float:
        """计算动态 Beta：逻辑深度高时加大语义权重"""
        if profile.logic_depth > 0.7:
            return 1.2  # 意图明确，加大权重
        elif profile.logic_depth < 0.4:
            return 0.8  # 意图模糊，降低权重
        return 1.0

Task R.3.3：实现核心标签系统

目标： 核心标签 1.33x 加权

新增文件： backend/app/services/core_tag_search.py

class CoreTagAwareSearch:
    """核心标签感知检索"""
    
    CORE_BOOST_FACTOR = 1.33  # 33% 加权
    
    async def search(
        self, 
        query: str, 
        user_id: str, 
        core_tags: list[str] = None,
        base_search_fn: callable
    ) -> list[SearchResult]:
        results = await base_search_fn(query, user_id)
        
        if core_tags:
            for r in results:
                meta = json.loads(r.metadata_ or "{}")
                chunk_tags = meta.get("tags", [])
                
                if any(tag in chunk_tags for tag in core_tags):
                    r.score *= self.CORE_BOOST_FACTOR
        
        return sorted(results, key=lambda x: x.score, reverse=True)

3. 修改现有文件

`backend/app/models/document.py`

增加 tags 和 is_core 字段：

class DocumentChunk(Base):
    # ... existing fields ...
    
    tags = Column(JSON, default=list)  # ["重要", "代码", "架构"]
    is_core = Column(Boolean, default=False)  # 是否核心切片

`backend/app/services/knowledge_service.py`

集成动态权重：

from app.services.query_analyzer import QueryAnalyzer
from app.services.dynamic_reranker import DynamicReranker
from app.services.core_tag_search import CoreTagAwareSearch

class KnowledgeService:
    def __init__(self, ...):
        # ... existing init
        self.query_analyzer = QueryAnalyzer()
        self.dynamic_reranker = DynamicReranker()
        self.core_tag_search = CoreTagAwareSearch()
    
    async def retrieve(self, query: str, user_id: str, ..., core_tags: list[str] = None) -> list[SearchResult]:
        # ... existing retrieval logic ...
        
        # 动态 Rerank
        results = self.dynamic_reranker.rerank(
            query, results, self.query_analyzer
        )
        
        # 核心标签加权
        if core_tags:
            results = await self.core_tag_search.search(
                query, user_id, core_tags, 
                lambda q, u: results  # 使用已检索的结果
            )
        
        return results

4. 新增测试

新增文件： backend/tests/services/test_dynamic_reranker.py

import pytest
from app.services.query_analyzer import QueryAnalyzer, QueryProfile
from app.services.dynamic_reranker import DynamicReranker

class TestQueryAnalyzer:
    def test_code_query_detection(self):
        analyzer = QueryAnalyzer()
        profile = analyzer.analyze("请解释这段 Python 代码")
        assert profile.is_code_related is True
    
    def test_table_query_detection(self):
        analyzer = QueryAnalyzer()
        profile = analyzer.analyze("统计这个 Excel 表格的总和")
        assert profile.is_table_related is True
    
    def test_conversational_detection(self):
        analyzer = QueryAnalyzer()
        profile = analyzer.analyze("我想了解一下")
        assert profile.is_conversational is True

class TestDynamicReranker:
    def test_code_query_weights(self):
        reranker = DynamicReranker()
        analyzer = QueryAnalyzer()
        
        profile = QueryProfile(
            logic_depth=0.5,
            is_code_related=True,
            is_table_related=False,
            keyword_density=0.3,
            is_conversational=False
        )
        
        weights = reranker._get_weights(profile)
        assert weights["keyword"] > weights["semantic"] * 0.5  # 代码查询关键词权重较高

5. 验收标准

查询特性分析准确（代码/表格/对话式识别）
动态权重根据查询类型调整
核心标签检索加权 1.33x
Rerank 集成测试通过

6. 变更文件清单

文件	操作	说明
`backend/app/services/query_analyzer.py`	新增	查询特性分析
`backend/app/services/dynamic_reranker.py`	新增	动态 Reranker
`backend/app/services/core_tag_search.py`	新增	核心标签检索
`backend/app/services/knowledge_service.py`	修改	集成动态权重
`backend/app/models/document.py`	修改	增加 tags/is_core 字段
`backend/tests/services/test_dynamic_reranker.py`	新增	动态 Reranker 测试

7. 工作量估算

任务	估算
R.3.1 查询特性分析	1 天
R.3.2 动态 Reranker	1 天
R.3.3 核心标签系统	1 天
测试 + 调试	1.5 天
R.3 总计	4.5 天

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